Popular GitHub Repositories for Grid Partitioning

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Official PyTorch implementation of VoxFormer [CVPR 2023 Highlight]

Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering"

A multi-platform desktop application to evaluate and compare LLM models, written in Rust and React.

Python library to easily log experiments and parallelize hyperparameter search for neural networks

Not only automatic, but also intelligent. An Intelligent data Visualization System, based on LLM.

Grid2Op a testbed platform to model sequential decision making in power systems.

Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.

POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.

A python package for simulating movement and spatial cell types (e.g. place cells, grid cells) in continuous environments.

SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving

Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"

A collection of algorithms used for any-angle pathfinding with visualisations.

Occ4cast: LiDAR-based 4D Occupancy Completion and Forecasting

An open-source platform for applying Reinforcement Learning for Grid Control (RLGC)

Papers on Graph Analytics, Mining, and Learning

This is an official implementation for "AutoFocusFormer: Image Segmentation off the Grid".

Source code for: Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds), accepted at ACCV 2018

Algorithm for prioritized multi-agent path finding (MAPF) in grid-worlds. Moves into arbitrary directions are allowed (each agent is allowed to follow any-angle path on the grid). Timeline is continuous, i.e. action durations are not explicitly discretized into timesteps. Different agents' size and moving speed are supported. Planning is carried out in (x, y, \theta) configuration space, i.e. agents' orientation are taken into account.

Talk2BEV: Language-Enhanced Bird's Eye View Maps (ICRA'24)